A language modeling approach to information retrieval

By Jay M. Ponte, W. Bruce Croft. 1998

Subfields: Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Anserini (2018, neutral): “Baselines appropriate to the subject of study need to be considered, of course, but researchers nearly always include comparisons against a bag-of-words ranking model such as BM25 [47] and query likelihood in the language modeling framework [45].”
Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects (2009, methodology): “In the paper, we compute the text relevancy of a query result by means of language models and a probabilistic ranking function that have sound foundations in statistical theory and have performed well empirically in many information retrieval tasks [22, 30].”
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models (2017, background): “One possible direction is to delve into the word weighting schemes such as [32, 35, 48] learned from the IRGAN generative retrieval model and then derive new ranking features on that basis.”
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey (2021, background): “The idea has its roots in the classical literature on probabilistic models for information retrieval that rank documents using language models [84, 139].”
Language modelling ’ s generative model : is it rational ? (2004, methodology): “The language modelling (LM) approach to speech recognition has been unexpectedly successful: this simple, statistically-based strategy, when supplied with enough training data, some smart algorithms, and hefty machine power has really delivered the goods (Young and Chase 1998).”
From Neural Re-Ranking to Neural Ranking: Learning a Sparse Representation for Inverted Indexing (2018, methodology): “QL The query likelihood retrieval model [40] with Dirichlet prior smoothing [60].”
Translation techniques in cross-language information retrieval (2012, methodology): “The language model was introduced by Ponte and Croft in 1998 [Ponte and Croft 1998] and has been extremely popular with IR researchers ever since [Miller et al.”
Discovering key concepts in verbose queries (2008, methodology): “where TM (ci) is a set of top M documents 3 retrieved in response to ci from collection C, and p(ci|·) is calculated using a maximum likelihood estimate [25].”
A probabilistic model of information retrieval: development and comparative experiments - Part 2 (2000, methodology): “Following the original Robertson/Sparck Jones model (with its assumptions of independence of terms), a substantial amount of work was done (e.g. by van Rijsbergen and colleagues, see Harper and van Rijsbergen (1978)) on formal models which made some attempt to avoid or relax such assumptions.”
Misinformation in Social Media: Definition, Manipulation, and Detection (2019, methodology): “Therefore, in contrast to using raw words, various methods test using latent semantic features that are learned from words, such as Language Modeling (LM) [28], word embeddings [33] and Topic Modeling [4].”
Learning to Rank for Information Retrieval and Natural Language Processing (2011, unknown): “In Language Model for IR (LMIR) [80, 113], the ranking model is defined as a conditional probability distribution P(q|d) where q denotes a query and d denotes a document.”
Combining the language model and inference network approaches to retrieval (2004, methodology): “In most cases, the underlying document model is assumed to be a multinomial distribution, although other distributions are possible, such as the multiple Bernoulli (Ponte & Croft, 1998).”
Diagnostic Evaluation of Information Retrieval Models (2011, background): “1975], probabilistic models [Amati and Rijsbergen 2002; Fuhr 1992; Lafferty and Zhai 2003; Ponte and Croft 1998; Robertson and Sparck Jones 1976; Turtle and Croft 2003; van Rijbergen 1977], and logic-based models [Fuhr 2001; van Rijsbergen 1986; Wong and Yao 1995].”
Statute Law Information Retrieval and Entailment (2019, background): “Another popular model for text retrieval is language model-based information retrieval [2].”